Background: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. Objective: This study examines whether sensor-augmented toys can be used to assess children’s fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. Methods: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called “Futuro Cube.” The game “roadrunner” focused on speed while the game “maze” focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05. Results: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046). Conclusions: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.

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中文翻译：

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使用传感器增强玩具评估儿童的精细运动技能：机器学习方法

背景：大约 5%-10% 的小学生表现出精细运动技能的延迟发展。为了解决这些问题，需要检测。当前的评估工具非常耗时，需要训练有素的主管，并且对儿童没有激励作用。传感器增强玩具和机器学习已被提出作为解决此问题的可能解决方案。目的：本研究探讨传感器增强玩具是否可用于评估儿童的精细运动技能。目标是 (1) 预测儿童运动评估电池第二版 (fine MABC-2) 精细运动技能部分的结果和 (2) 研究分类模型、游戏、数据类型和游戏对预测的难度级别。方法：小学儿童（n=95，7.8 岁 [SD 0. 7] 年）表演了精美的 MABC-2，并用一种​​名为“Futuro Cube”的传感器增强玩具玩了 2 场游戏。游戏“roadrunner”注重速度，而游戏“迷宫”注重精度。每场比赛都有几个难度级别。在玩游戏时，传感器和游戏数据都被收集。四个受监督的机器学习分类器使用这些数据进行训练，以预测精细的 MABC-2 结果：k-最近邻 (KNN)、逻辑回归 (LR)、决策树 (DT) 和支持向量机 (SVM)。首先，我们比较了游戏和分类器的性能。随后，我们比较了在准确率和 F1 分数上表现最好的分类器和游戏的难度级别和数据类型。对于所有统计检验，我们使用 α=.05。结果：达到的最高平均准确度 (0. 76) 是使用 DT 分类器实现的，该分类器对传感器和游戏数据进行了训练，这些数据是从玩最简单和最难的 Roadrunner 游戏中获得的。在从跑酷游戏和迷宫游戏中获得的数据之间的准确度得分中发现了显着的性能差异（DT，P=.03；KNN，P=.01；LR，P=.02；SVM，P=.04）。对于跑酷游戏（DT vs KNN，P=.42；DT vs LR，P=.35；DT vs SVM，P =.08) 和迷宫游戏（DT 对 KNN，P=.15；DT 对 LR，P=.62；DT 对 SVM，P=.26）。使用传感器和从跑酷游戏获得的游戏数据训练的 DT 分类器获得的最佳表现难度级别（最简单和最难级别的组合）的准确性显着优于最简单和中等级别的组合（P = .046）。结论：我们的研究结果表明，传感器增强玩具可以有效预测小学儿童的 MABC-2 分数。选择游戏类型（关注速度或精度）和数据类型（传感器或游戏数据）对于确定性能比选择机器学习分类器或难度级别更重要。我们的研究结果表明，传感器增强玩具可以有效预测小学儿童的 MABC-2 分数。选择游戏类型（关注速度或精度）和数据类型（传感器或游戏数据）对于确定性能比选择机器学习分类器或难度级别更重要。我们的研究结果表明，传感器增强玩具可以有效预测小学儿童的 MABC-2 分数。选择游戏类型（关注速度或精度）和数据类型（传感器或游戏数据）对于确定性能比选择机器学习分类器或难度级别更重要。

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